kaikai luo’s Post

Recognizing Everything from All Modalities at Once: https://buff.ly/3z0E69T A) WHAT PROBLEM DOES THAT SOLVE? The study addresses extracting and understanding information from multiple media types (text, audio, image, and video) simultaneously. Traditional information extraction systems focus on a single modality. This study aims to unify extraction across modalities and ground information contextually within each modality, creating a system that understands multimodal inputs as a cohesive unit. B) HOW DOES IT SOLVE THE PROBLEM? The study introduces REAMO (Recognizing Everything from All Modalities at Once), a multimodal large language model for Universal Information Extraction across modalities. REAMO integrates: 1. Multimodal Encoder: Encodes images, videos, and audio for the language model. 2. LLM Reasoner: Uses Vicuna language model to understand and reason about content. 3. Decoding and Grounding: Implements segmentation for image and audio grounding. REAMO processes text, audio, image, and video inputs, extracting relevant information and contextual grounding. It employs instruction tuning, alignment learning, and grounding-aware tuning to enhance multimodal understanding. C) EXPLAIN A) & B) TO A TEENAGER: Imagine a super-smart computer that can read, watch, and listen simultaneously. It understands books, videos, music, and conversations together as one story instead of separately. This computer uses REAMO, with powers to understand pictures/videos, sounds, and words together. REAMO figures out what's happening across media, like who's talking in a video based on the story. D) FOR BUSINESSES, THIS TECHNOLOGY ENABLES: 1. Enhanced Data Processing: Efficiently process multimodal data from sources like social media, videos, and audio recordings. 2. Improved Customer Insights: Gain deeper understanding of customer behavior and preferences by integrating information across modalities. 3. Automation and Efficiency: Automate extraction and grounding of multimodal information, reducing manual processing costs. 4. Innovative Applications: Enable advanced virtual assistants, interactive marketing campaigns, and enhanced accessibility features through holistic multimodal understanding. Overall, REAMO represents substantial AI progress, offering businesses powerful tools to harness their multimodal data's full potential.

🚀 Llama 3.1 vs. GPT-4: A Comparative Insight for Real-World Applications 🚀 In the dynamic landscape of AI technology, two advanced language models are making waves: Llama 3.1 and GPT-4 (including GPT-4o). Here’s a quick comparative overview based on their capabilities and real-world performance: Performance Comparison 🔍 Reasoning and Coding: Llama 3.1 excels in reasoning tasks and coding, often matching or surpassing GPT-4. It's a powerhouse for applications requiring complex reasoning and programming support. 📜 Context Length: With a context length of 128K tokens, Llama 3.1 handles extensive inputs better than GPT-4, maintaining coherence over longer texts and conversations. 🎯 Accuracy and Versatility: GPT-4o shines with its accuracy and fine-tuning capabilities, excelling across various domains. It's a go-to for both creative writing and technical documentation. Llama 3.1 is highly capable but may not match GPT-4o in nuanced language tasks. Application Suitability 💬 Real-Time Applications: Llama 3.1’s optimized performance makes it ideal for real-time applications like customer support chatbots, offering quick and efficient responses. 🎨 Creative and Technical Domains: GPT-4o is favored for deep language understanding, making it perfect for content creation and complex problem-solving. Its versatility is unmatched, catering to a wide range of applications. 🌐 Multilingual Capabilities: Llama 3.1 supports multiple languages effectively, making it a strong choice for businesses operating in diverse linguistic environments. Choosing between Llama 3.1 and GPT-4 depends on your specific needs: Llama 3.1: Ideal for fast, contextually aware responses. GPT-4o: Excels in accuracy and versatility across a broader range of tasks. Both models bring unique strengths to the table, driving innovation in AI technology. 🧠💡 #AI #MachineLearning #Llama3 #GPT4 #TechInnovation #ArtificialIntelligence #CustomerSupport #ContentCreation #MultilingualSupport

In the rapidly evolving landscape of Large Language Models (#LLMs), the art of prompt engineering emerges as a critical skill set for optimizing AI performance. It's not merely about asking questions but intricately guiding the AI to produce desired outcomes. For data science and AI development professionals, understanding how to craft, evaluate, and manage prompts is essential. This encompasses structuring inputs that align with specific tasks and refining and tuning these prompts through manual and automated methods to enhance model responsiveness. Moreover, navigating challenges such as prompt overfitting, ambiguity, and maintaining contextual relevance requires a blend of technical expertise, creativity, and strategic thinking. As we explore advanced prompt tuning techniques and integrate metrics for tuning success, we are charting a future where prompt engineering is both an art and a science, pivotal in harnessing the full potential of LLMs for diverse applications. #LLMInnovations #LanguageModelMagic #AIIntelligence #NextGenAI #MachineLearningMastery #PromptEngineering #FutureOfCoding #TextGenerationTech #AIResearchRevealed #GPTGenius

Demystifying Large Language Models: A Guide for Everyone Ever heard of large language models (LLMs)? They're a type of AI that's been making waves in the tech world, and for good reason. LLMs are trained on massive amounts of text data, allowing them to generate text, translate languages, write different kinds of creative content, and even answer your questions in an informative way. But what exactly are LLMs, and how do they work? In this post, we'll break down the key concepts of LLMs in a way that's easy to understand, regardless of your technical background. What are LLMs? Imagine a machine that can read and understand text like a human, and even generate its own text that sounds natural. That's essentially what an LLM is. By analyzing massive amounts of text data, LLMs learn the patterns and nuances of language, allowing them to perform a variety of tasks. What can LLMs do? The possibilities are endless! Here are just a few examples: Generate realistic and creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. Translate languages fluently and accurately. Answer your questions in an informative way, even if they are open ended, challenging, or strange. Write different kinds of creative content, like poems, code, scripts, musical pieces, email, letters, etc. The future of LLMs LLMs are still under development, but they have the potential to revolutionize the way we interact with technology. From personalized education to more efficient communication, the possibilities are vast. So, keep an eye on LLMs – they're definitely here to stay! #machinelearning #artificialintelligence #bigdata #languagemodels #futureoftech I hope this post helps you understand the exciting world of large language models! If you have any questions, feel free to leave a comment below.

Dive into the world of advanced language models with confidence! Our guide will help you navigate and make the most of these powerful tools. Large language models are reshaping how we interact with technology, offering capabilities that streamline tasks from coding to content creation. But to tap into their full potential, it's crucial to know how to use them right. Here's a practical rundown on getting started. Understanding what large language models can do is step one. They're trained on vast amounts of text data, enabling them to perform complex tasks by recognizing patterns and context. This includes generating code, powering chatbots, and crafting content that seems almost human-made. So how do we put these models to work effectively? - **Picking the Perfect Model**: It’s all about finding the right fit. While some models boast speedier training times or more precise outcomes, others shine when processing lengthy sequences quickly. Do your homework to identify which model aligns with your goals. - **Resource Readiness**: Remember, running sophisticated models like Mixtral 8x22B isn't light on resources; you'll need serious computing power or perhaps a cloud-based alternative if your own setup falls short. - **Application Optimization**: Every model has its niche—some excel in generating code, while others are better suited for tasks involving both vision and language. Choose one that complements what you aim to achieve. - **Keeping Up With Open Source**: Stay in the loop with open-source releases from various organizations—they're often at the forefront of innovation and available for anyone eager to explore cutting-edge tools. - **Balancing Costs**: Performance comes at a price but weigh it against your budget constraints. You might find that less expensive options provide comparable quality without breaking the bank. - **Fine-Tuning Trials**: Don't shy away from fine-tuning these models for particular tasks—it could be just what you need to elevate their performance even further. In wrapping up, remember that large language models hold incredible promise for those willing to learn their intricacies. Keep exploring, stay informed about new developments, and don't hesitate to experiment—you might just unlock new frontiers in tech efficiency. #AILanguageModels #MachineLearning #TechInnovation

A new paper got released by Meta and for me it is an interesting research direction. Paper name: Self-Rewarding Language Models 1. Main Vision of the Paper: A new concept where language models generate and evaluate their own training data, leading to continuous self-improvement in performance. 2. What is the current problem: Conventional language model alignment often relies on fixed reward models based on human preferences, limiting the model's potential to human performance levels. Both RLHF and DPO is bottlenecked by the size and quality of the human preference data. 3. Addressing the Problem: The paper addresses this limitation by proposing an innovative training approach where the language model acts as its own judge, generating and evaluating new training data iteratively. This self-rewarding mechanism enables the model to continually refine its performance and reward modeling ability, transcending the constraints of human-based training data and fostering a cycle of ongoing enhancement. Algorithm overview: Starting Point: 1. Begin with the Llama 70B model, already fine-tuned on OpenAssistant data, which provides a strong foundation in understanding and generating human-like responses. 2. Generation and Evaluation: The model generates a variety of responses to a single prompt, such as "Suggest ways to reduce carbon footprint in daily life." Multiple creative and practical ideas are produced by the model. 3. Self-Judging: Using the same model, each response is evaluated and ranked based on criteria like feasibility, environmental impact, and originality. 4. Training with DPO: Direct Preference Optimization (DPO) is applied, using the rankings from step 3 to re-train the model, enhancing its response quality and judgment accuracy. 5. Iterative Process: Steps 2 to 4 are repeated for three iterations, leading to progressive refinement in the model's ability to generate and evaluate responses, thus improving both its creative generation and evaluative skills. This approach is a very innovative way to solve the dependency on human feedback and I think the future is a hybrid approach of using both human-preference and LLM as a judge model. I have a few concerns on the approach especially how it can be used for addressing magnifying bias while training. At HTCD, we have developed a similar algorithm that uses a combination of Large Language Models (LLMs) to create a self-rewarding loop. This loop enhances the accuracy of a smaller LLM in a specific domain. [More details very soon] Link to the paper is in comments. #knowledgesharing #generatieveai

🚀 Excited to present our work “Large Language Models Can Self-Improve At Web Agent Tasks”! We show that synthetic data self-improvement boosts task completion by 31% on WebArena and introduce quality metrics for measuring autonomous agent workflows. Training LLMs to act as effective web agents has been challenging due to a lack of training data. Our research demonstrates that LLMs can navigate and perform actions in complex environments guided by natural language instructions. We explored self-improvement techniques where LLMs fine-tune on data they generate themselves, assessing their improvement over the base model on the WebArena benchmark. This includes tasks like posting on subreddits, navigating GitLab, and shopping sites. We tested three synthetic training data mixtures (in-domain, out-of-domain, and mixed data) and found that all improved performance. The best results came from a mixture of in-domain and out-of-domain examples, significantly boosting capabilities and quality. To measure improvement, we developed new metrics assessing performance and capabilities. We extended the VERTEX score via Dynamic Time Warping for variable trajectory comparisons, providing deeper insights beyond aggregate-level completion scores. Our findings suggest that LLMs can acquire new capabilities, with net gains observed in all self-improved models. The self-improved agents also showed increased robustness, especially in functional correctness, ensuring stable performance. We provide new insights into unsupervised self-improvement techniques on complex, multi-step agent environments, such as web environments for LLM-based agent workflows. Overall, we demonstrate how our approach can help improve autonomous (web) agents. 🙏 A huge thank you to our amazing team and to the outstanding collaboration between the University of Pennsylvania, Johannes Kepler Universität Linz and ExtensityAI: Ajay Patel, Markus Hofmarcher, Claudiu Leoveanu-Condrei, Chris Callison-Burch, Sepp Hochreiter 📄 Read more in our paper on arXiv: https://lnkd.in/dPuzTWB6 💻 Check out our code on GitHub: https://lnkd.in/d5VAf-kj #AI #MachineLearning #LLMs #Agents

#copied Here's what to know about 𝗢𝗽𝗲𝗻𝗔𝗜'𝘀 𝗹𝗮𝘁𝗲𝘀𝘁 𝗺𝗼𝗱𝗲𝗹, 𝗚𝗣𝗧-𝟰𝗼, illustrated👇👇👇 The latest model GPT-4o ("o" stands for omni) excels in an array of tasks compared to its previous counterpart, GPT-4 Turbo. Here are key specs: 𝟭. 𝗠𝘂𝗹𝘁𝗶-𝗺𝗼𝗱𝗮𝗹𝗶𝘁𝘆 Unlike GPT-4T which required separate models per data type, GPT-4o processes text, voice and vision in a single model. This greatly improves comprehension and reduces latency from text-to-voice, voice-to-voice and etc. 𝟮. 𝗖𝗼𝘀𝘁-𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 GPT-4o is 50% cheaper than GPT-4 turbo. For instance, the cost of 1M token (equivalent to about ~700K words) for GPT-4 Turbo is $10 for input and $30 for output. GPT-4o is fraction of that cost, $5 for input and $15 for output. From a developer's perspective, I'm loving this! 𝟯. 𝟱𝟬+ 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 GPT-4o is much more flexible in multi-lingual capability as it now supports more than 50+ languages. Plus, it has shown to be better in non-English tasks. 𝟰. 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝘀𝗽𝗲𝗲𝗱 There's reduce latency in generating tokens. It's 2x faster in speed. I had a chance to test this in Open AI's playground, and, in deed, I do see a sharp contrast in the text generation speed for GPT-4o vs GPT-4 turbo for the same prompt. 𝟱. 𝗛𝗶𝗴𝗵𝗲𝗿 𝗿𝗮𝘁𝗲 𝗹𝗶𝗺𝗶𝘁 There's increased rate limit for requests-per-minute (RPM) and tokens-per-minute (TPM). This will be useful especially for high chatbots with high QPS. 𝟲. 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 Across text tasks, GPT-4o has shown to achieve 88.7% MMLU (vs 86.5% for GPT-4T and 83.7% for Gemini Ultra), 76.6% MATH (vs 72.6% GPT-4T and 53.2% for Gemini Ultra). Also, it has shown improved evaluations in audio ASR, translation, zero-shot and vision understanding. 𝟳. 𝗦𝗮𝗳𝗲𝘁𝘆 They used 70+ domain experts across social psychology, fairness and bias, and misinformation to identify and mitigate risks. *I'd have to say that from a consultant's standpoint, I'm looking forward to getting my hands on with the latest model's API and see its fortes and weaknesses in prompt response for RAG-based, data analysis tasks. 2X gain in latency with 50% cheaper in token input/output is quite huge from business perspective as well.

📅 July 10, 2024 AIBuzzWorld Daily Newsletter! Dive into the fascinating world of Artificial Intelligence and be the first to learn about the latest AI news: 1. **LLMs Struggle with Book-Length Text**      • Current long-context LLMs struggle to understand and reason over book-length texts.   • Researchers created NOCHA, a dataset to test LLM's comprehension of lengthy narratives.   • Even advanced LLMs like GPT-4o achieved only 55.8% accuracy.   • Readmore: https://lnkd.in/g2tFiH5g 2. **Quora’s Poe Introduces Artifacts-Like Feature**      • Quora’s Poe now has a feature to create custom web apps within the chat.   • This feature works well with LLMs that excel at coding.   • Users can create interactive experiences like games and data visualizations.   • Readmore: https://lnkd.in/gSJffbB9 3. **New Features in Ollama 0.2**      • Ollama 0.2 now supports multiple chat sessions and running various models simultaneously.   • Users can load different models for tasks like RAG and running agents.   • This update improves efficiency and multitasking.   • Readmore: https://lnkd.in/g-BCN4zH 4. **Wheebot for Quick Landing Page Creation**      • Wheebot allows users to create and edit landing pages via WhatsApp.   • Users can describe their requirements in plain English.   • Wheebot generates and updates sites instantly through encrypted chats.   • Readmore: https://lnkd.in/gsRtzGrd #AI #ArtificialIntelligence #MachineLearning #LLM #Microsoft #Quora #ClaudeAI #Wheebot #TechNews #Innovation #AIUpdates #FutureTech